TY - GEN

T1 - Change-point detection in time-series data by direct density-ratio estimation

AU - Kawahara, Yoshinobu

AU - Sugiyama, Masashi

PY - 2009/12/1

Y1 - 2009/12/1

N2 - Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel non-parametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate the ratio of probability densities, not the probability densities themselves. This formulation allows us to avoid non-parametric density estimation, which is known to be a difficult problem. We provide a change-point detection algorithm based on direct density-ratio estimation that can be computed very efficiently in an online manner. The usefulness of the proposed method is demonstrated through experiments using artificial and real datasets.

AB - Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel non-parametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate the ratio of probability densities, not the probability densities themselves. This formulation allows us to avoid non-parametric density estimation, which is known to be a difficult problem. We provide a change-point detection algorithm based on direct density-ratio estimation that can be computed very efficiently in an online manner. The usefulness of the proposed method is demonstrated through experiments using artificial and real datasets.

UR - http://www.scopus.com/inward/record.url?scp=72849146898&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=72849146898&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:72849146898

SN - 9781615671090

T3 - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics

SP - 385

EP - 396

BT - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133

T2 - 9th SIAM International Conference on Data Mining 2009, SDM 2009

Y2 - 30 April 2009 through 2 May 2009

ER -